Parallel Computing With FPGAs–Concepts and Applications

Parallel computing has become an essential subject in the field of computer science and also it is shown to be critical when researching in high end solutions. The evolution of computer architectures (multicore and manycore) towards an increased quantity of cores, where parallelism could be the approach to option for speeding up an algorithm within the last few decades, the graphics processing unit, GPU and CPU, has gained an essential place in the area of high end computing (HPC) due to its low priced and massive processing power that is parallel. In this paper, we survey the idea of parallel computing, especially CPU computing and its programming models and also gives a couple of theoretical and technical concepts which can be often needed to understand the CPU and GPU as well as its parallelism in massive model. In particular, we show how this technology is new in assisting the field of computational physics, especially when the issue is data parallel.

Parallel computing is emerging as an important area of research in computer architectures and software systems. Many algorithms can be greatly accelerated using parallel computing techniques. Specialized parallel computer architectures are used for accelerating specific tasks. High-Energy Physics Experiments measuring systems often uses FPGAs for fine-grained computation. FPGA combines many benefits of both software and ASIC implementations. Like software, the mapped circuit is flexible, and can be reconfigured over the lifetime of the system. FPGAs therefore have the potential to achieve far greater performance than software as a result of bypassing the fetch-decode-execute operations of traditional processors, and possibly exploiting a greater level of parallelism. Creating parallel programs implemented in FPGAs is not trivial. This paper presents existing methods and tools for fine-grained computation implemented in FPGA using High Level Programming Languages.

Undergraduate Topics in Computer Science, 2018

Undergraduate Topics in Computer Science (UTiCS) delivers high-quality instructional content for undergraduates studying in all areas of computing and information science. From core foundational and theoretical material to final-year topics and applications, UTiCS books take a fresh, concise, and modern approach and are ideal for self-study or for a one-or two-semester course. The texts are all authored by established experts in their fields, reviewed by an international advisory board, and contain numerous examples and problems. Many include fully worked solutions.

1996

International Journal of Parallel Programming, 2013

This special issue provides a forum for presenting the latest research on algorithms and applications for parallel and distributed systems, including algorithm design and optimization, programming paradigms, algorithm design and programming techniques heterogeneous computing systems, tools and environment for parallel/distributed software development, petascale and exascale algorithms, novel parallel and distributed applications, and performance simulations, measurement, and evaluations. The success of parallel algorithms-even on problems that at first glance seem inherently serialsuggests that this style of programming will be the inherent to any application in a near future. The relevant research has gained momentum with multicore and manycore architectures, and with the expected arrival of exascale computing. As a result, the space of potential ideas and solutions is still far from being widely explored.

Texts in Computational Science and Engineering, 2010

Acta Informatica, 1976

This paper presents a model of parallel computing. Six examples illustrate the method of programming. An implementation scheme for programs is also presented. t

Lcpc, 1995

It is our pleasure to present the papers from the 20th International Workshop on Languages and Compilers for Parallel Computing! For the past 19 years, this workshop has been one of the primary venues for presenting and learning about a wide range of current research in parallel computing. We believe that tradition has continued in this, the 20th year of the workshop.

Lecture Notes in Computer Science, 2006

Welcome to the proceedings of the 4th International Symposium on Parallel and Distributed Processing and Applications (ISPA 2006), which was held in Sorrento, Italy, December, 4-6 2006. Parallel computing has become a mainstream research area in computer science and the ISPA conference has become one of the premier forums for the presentation of new and exciting research on all aspects of parallel and distributed computing. We are pleased to present the proceedings for ISPA 2006, which comprises a collection of excellent technical papers and keynote speeches. The accepted papers cover a wide range of exciting topics including architectures, languages, algorithms, software, networking and applications. The conference continues to grow and this year a record total of 277 manuscripts were submitted for consideration by the Program Committee. From these submissions the Program Committee selected only 79 regular papers in the program, which reflects the acceptance rate as 28%. An additional 10 workshops complemented the outstanding paper sessions. The submission and review process worked as follows. Each submission was assigned to at least three Program Committee members for review. Each Program Committee member prepared a single review for each assigned paper or assigned a paper to an outside reviewer for review. In addition, the Program Chairs and Program Vice-Chairs read the papers when a conflicting review result occurred. Finally, after much discussion among the Program Chairs and Program Vice-Chairs, based on the review scores, the Program Chairs made the final decision. Given the large number of submissions, each Program Committee member was assigned roughly 7-12 papers. The excellent program required a lot of effort from many people. First, we would like to thank all the authors for their hard work in preparing submissions to the conference. We deeply appreciate the effort and contributions of the Program Committee members who worked very hard to select the very best submissions and to put together an exciting program. We are also very grateful to the keynote speakers for accepting our invitation to present keynote talks. Thanks go to the Workshop Chairs for organizing ten excellent workshops on several important topics related to parallel and distributed computing and applications.

Parallel computing is critical in many areas of computing, from solving complex scientific problems to improving the computing experience for smart device and personal computer users. This study investigates different methods of achieving parallelism in computing, and presents how parallelism can be identified compared to serial computing methods. Various uses of parallelism are explored. The first parallel computing method discussed relates to software architecture, taxonomies and terms, memory architecture, and programming. Next parallel computing hardware is presented, including Graphics Processing Units, streaming multiprocessor operation, and computer network storage for high capacity systems. Operating systems and related software architecture which support parallel computing are discussed, followed by conclusions and descriptions of future work in ultrascale and exascale computing.

2009

The success of the gaming industry is now pushing processor technology like we have never seen before. Since recent graphics processors (GPU’s) have been improving both their programmability as well as have been adding more and more floating point processing, it makes them very appealing as accelerators for generalpurpose computing. This minisymposium gives an overview of some of these advancements by bringing together experts working on the development of techniques and tools that improve the programmability of GPU’s as well as the experts interested in utilizing the computational power of GPU’ scientific applications. This first EuroGPU Minisymposium brought together severl experts working on the development of techniques and tools that improve the programmability of GPU’s as well as the experts interested in utilizing the computational power of GPU’s for scientific applications. This short summary thus gives a very useful, but quick overview of some of the major recent advancemen...

Lecture Notes in Computer Science, 2002

Parallel processing offers enhanced speed of execution to the user and facilitated by different approaches like data parallelism and control parallelism. Graphic Processing Units provide faster execution due to dedicated hardware and tools. This paper presents two popular approaches and techniques for distributed computing and GPU computing, to assist a novice in parallel computing technique. The paper discusses environment needs to be setup for both the above approaches and as a case study demonstrate matrix multiplication algorithm using SIMD architecture.

1996

These lecture notes under development and constant revision, like the eld itself have been used at MIT in a graduate course rst o ered by Alan Edelman and Shang-Hua Teng during the spring of 1994 MIT 18.337, Parallel Scienti c Computing. This rst class had about forty students from a variety of disciplines which include Applied Mathematics, Computer Science, Mechanical Engineering, Chemical Engineering, Aeronautics and Aerospace, and Applied Physics. Because of the diverse backgrounds of the students, the course, by necessity, w as designed to be of interest to engineers, computer scientists, and applied mathematicians. Our course covers a mixture of material that we feel students should be exposed to. Our primary focus is on modern numerical algorithms for scienti c computing, and also on the historical trends in architectures. At the same time, we h a ve always felt that students and the professors must su er through hands-on experience with modern parallel machines. Some students enjoy ghting new machines; others scream and complain. This is the reality of the subject. In 1995, the course was taught again by Alan Edelman with an additional emphasis on the use of portable parallel software tools. The sad truth was that there were not yet enough fully developed tools to be used. The situation is currently improving. During 1994 and 1995 our students programmed the 128 node Connection Machine CM5. This machine was the 35th most powerful computer in the world in 1994, then the very same machine was the 74th most powerful machine in the spring of 1995. At the time of writing, December 1995, this machine has sunk to position 136. The fastest machine in the world is currently in Japan. In the 1996 course we used the IBM SP-2 and Boston University's SGI machines. In addition to coauthors Shang-Hua Teng and Robert Schreiber, I would like to thank our numerous students who have written and commented on these notes and have also prepared many o f the diagrams. We also thank the students from Minnesota SCIC 8001 and the summer course held at MIT, Summer 6.50s also taught b y Rob Schreiber for all of their valuable suggestions. These notes will probably evolve i n to a book which will eventually be coauthored by Rob Schreiber and Shang-Hua Teng. Meanwhile, we are fully aware that the 1996 notes are incomplete, contain mathematical and grammatical errors, and do not cover everything we wish. They are an improvement o ver the 1995 notes, but not as good as the 1997 notes will be. I view these notes as a basis on which t o improve, not as a completed book. It has been our experience that some students of pure mathematics and theoretical computer science are a bit fearful of programming real parallel machines. Students of engineering and computer science are sometimes intimidated by mathematics. The most successful students understand that computing is not dirty" and mathematical knowledge is not scary" or useless," but both require hard work and maturity to master. The good news is that there are many jobs both in the industrial and academic sectors for experts in the eld! A good course should have a good theme. We try to emphasize the fundamental algorithmic ideas and machine design principles. We h a ve seen computer vendors come and go, but we believe that the mathematical, algorithmic, and numerical ideas discussed in these notes provide a solid foundation that will last for many y ears.